Introduction

Through funding from the Australian Urban Research Infrastructure Network (AURIN) an automated open-source tool was created to generate walkability indices at user-specified scales (i.e., suburb, Census Collector District and user-specified road network buffers). The methods in these tools are able to utilise available state and national level geospatial data to create walkability indices for all Australian urban areas.

This document provides an overview of the walkability tools and guidance on the use of the workflows in the AURIN Portal. It first provides a background to the benefits of walkable environments, and then describes some of the methods of measuring walkability, the base data required to create these measures and how these have been implemented in the AURIN Portal.

Background to Walkability

Physical inactivity is the fourth leading contributor to disease globally1 and increasing physical activity is a priority, both within Australia and internationally. Given the recognized health benefits associated with active lifestyles, and the significant benefits, and cost savings that can be derived from even modest increases in physical activity, identifying factors that facilitate activity is essential. One approach is through the design of urban environments which promote walking whilst also providing a range of co-benefits including increased social interaction, sustainability and environmental protection2. Whilst a growing priority, creating more walkable neighbourhoods is a challenge requiring evidence and advocacy to help change policy, urban design, transportation practice and public opinion. Indeed the OECD has called for leadership by transport, land use and health ministers to provide ‘the necessary, legal, administrative and technical frameworks’ to support and encourage active modes of transport such as walking (OECD, 2012). However, previously there has been no readily or broadly accessible, spatial analytic tool available to assess the walkability of Australian urban areas in order to promote walking. The AURIN system fills that gap, providing a set of tools for examining walkability at varying scales across Australia.

Measures of Walkability

An important advance in this research field was provided by a US-based urban planning academic, Larry Frank,3-5 who developed a Geographic Information System (GIS)-based walkability index to predict walking for transportation purposes. This index was initially adopted in Australia by Owen and colleagues6 and further developed by the Centre for Built Environment and Health (CBEH) at The University of Western Australia. Initially, the CBEH team explored methodological issues related to the use of pedestrian and street networks7 as well as the influence of neighbourhood scale8 on calculating measures of walkability. The CBEH team also explored the impact on behaviour of incorporating different types of land uses in a land use mix variable9. Thus, in extending Larry Frank’s initial work, the CBEH team has adapted the component measures of street connectivity, net dwelling density and land use mix to utilise State Government data sets and has developed algorithms for walkability indices for both transport walking and recreational walking9.

Previously the analytical tools used to analyse walkability were commercial GIS software packages such as ESRI’s ArcGIS for Desktop. Such commercial packages offer high levels of functionality and potential for customisation but come with the overheads of price and steep learning curves for new users. The AURIN Walkability Tools attempts to overcome these limitations and facilitate wider walkability analyses by providing a reasonably simple set of user interface tools.

Currently, a web-based tool, which assesses the accessibility of transit facilities and retail outlets and their proximity to an address of interest, providing a score of walkability, has been developed by Walk Score10 (see http://www.walkscore.com/). However, this application uses Google Maps (and other data sources) as a platform for analysis and in the case of Australia, retail destinations are based on a commercial data set originally compiled by SENSIS and purchased by Google and now maintained by them. The CBEH team has undertaken a validity study of the SENSIS data and identified short comings in its completeness, which reduces the usefulness of these data for examining the walkability of neighbourhoods. AURIN’s Walkability workflow attempts to overcome this problem by providing access to licensed data sets from PSMA and the Australian Bureau of Statistics and providing the ability for users to upload data they have compiled themselves. Moreover, as AURIN’s reach expands, it is likely to extend the data sets available in its portal to include more State-based fine grained data bases such as the Valuer General data set.

Spatial Units of Analysis

A number of units of analysis have been tested against walkability measures. However, Learnihan and colleagues at CBEH found that the more fine grained the scale, the stronger the associations with walking outcomes. Thus, most researchers now use scales of analysis that represent the geographic area that study participants are exposed in their daily lives around their residential locations, which can be thought of as participants’ ‘neighbourhood’. Nevertheless, different users may have different needs in terms of describing the walkability of an area. Thus, it was decided that the AURIN tool should maximise flexibility, allowing the user to select their own scale of analysis.

A number of units of analysis have been tested against walkability measures. However, Learnihan and colleagues8 at CBEH found that the more fine grained the scale, the stronger the associations with walking outcomes. Thus, most researchers now use scales of analysis that represent the geographic area that study participants are exposed in their daily lives around their residential locations, which can be thought of as participants’ ‘neighbourhood’. Nevertheless, different users may have different needs in terms of describing the walkability of an area. Thus, it was decided that the AURIN tool should maximise flexibility, allowing the user to select their own scale of analysis.